Case study · Fintech · Bengaluru
Keeper saves + Memory that stops false churn signals
Fintech · retention defense with shared Revenue Memory
Revenue protected (90d)
₹9.6L
Save plays completed
14
Keeper re-engage + SMS check-in
False churn signals dropped
38%
Memory de-weighted pattern
Scout re-prioritization
3×
Handoff quality vs prior quarter
Challenge
RevOps burned cycles on usage dips that did not churn. Scout kept re-targeting saved accounts because siloed play data never fed back. Board asked for defended ₹, not health scores.
Approach
- →Scale tier: full Scout → Closer → Keeper → Grower loop with Customer Revenue DNA™ on top accounts.
- →Keeper SMS check-in on re-engage plays; Memory episodes on every save with executive outcome = Protected.
- →Keeper → Scout handoff exported churn learnings — Scout elevation rules updated within the same workspace.
Executive outcomes (90d)
- Pipeline
- ₹7.2L new (expansion-ready cohort)
- Protected
- ₹9.6L across 4 recovered customers
- At risk
- ₹3.1L still defended (open re-engage)
- Expansion
- ₹4.8L expansion MRR influenced
“One memory layer across Scout and Closer — when Keeper saves an account, Scout stops re-chasing the same false churn signal.”
Permissioned design-partner narrative. HubSpot sync on Growth+ India workforce tier.
Stack: HubSpot · Gupshup WhatsApp · Wavly Memory API · 90-day design-partner pilot · Scale tier